Automated data management

Among the most obvious benefits of augmented analytics is the opportunity to reduce tedium and save time. With the rise in data quantity ushered in by the big data era came a concomitant rise in the amount of labor required to prepare that data for analysis and consolidate insights from disparate data sources.
 

Data preparation

To be processed by machine learning algorithms, data must be collected from multiple sources, organized and aggregated, then formatted cleanly and consistently. When dealing with tabular data sets, for example, data fields must be ordered consistently to help ensure that the respective features of vector embeddings for each data point correspond with those of other data points. When done manually, this can be a very tedious and labor-intensive process.

Modern, AI-enhanced BI platforms can use machine learning to automate many data cleaning tasks by:

• Automatically detecting relevant data attributes. For instance, an algorithm might detect the presence of geographic information (such as a postal code or latitude and longitude coordinates) or contact information (such as an email address or phone number). These data features can then be placed within a unified formatting scheme.

• Ensuring data quality and preserving privacy. Algorithms can be trained to weight, de-emphasize or disregard input from different data sources in accordance with an organization’s data governance policy. An augmented analytics platform can further enforce data governance practices by, for example, automatically scrubbing data points of personally identifying information (PII). This is particularly helpful in fields such as healthcare, in which the use of such information is heavily regulated.

• Reading and extracting information from a PDF or rich text sources. This process can also entail the removal or substitution of null values or special characters, such as punctuation marks or nonstandard symbols, in order to convert text to a machine-readable format.

• Indexing and grouping related information. For example, an algorithm might recognize the presence of parallel data points in different sources and suggest aggregation or detect redundant data points and automatically combine them into a single entry. Furthermore, an augmented analytics platform may integrate a model capable of producing effective vector embeddings for each document to enable efficient vector search and retrieval augmented generation.

Data discovery

Though the work typically associated with data analysts usually entails proactive querying of data to inform specific decisions or test specific hypotheses, much of the value offered by robust data science comes from exploring data at large for unseen or unexpected connections and insights.  

An augmented analytics tool might, for example, automatically recommend associations between different data sources that a user might otherwise have overlooked or surface outliers or anomalous trends for further analysis. The application of unsupervised learning, such as association or clustering models, can recognize inherent patterns and correlations that might inform actionable insights.

Natural language interactions

One of the most important barriers to entry for nontechnical users interested in data analytics is the depth of specialized technical knowledge required for traditional data analytics. For many individuals, learning to code or use structured query language (SQL) is prohibitively difficult or time-intensive. This includes learning the statistical techniques, nomenclature and best practices required to effectively interpret and validate results.

The marriage of data analytics with natural language processing (NLP) is perhaps the single most impactful and intuitive way that augmented analytics broadens access to data-driven insights. Users can query data using plain, simple language—“which products have the highest return rates in the 30 days following the holiday season?”—and receive responses in similarly straightforward language.

On the back end, an LLM must interpret that natural language query, translate it into a structured request and making assumptions to fill in missing information based on its understanding of the context of the user’s question. One or more models must be selected to process the request. The model must draw the data source (or sources) most pertinent to the matter. Finally, the LLM must interpret the mathematical results and articulate them in a way that centers relevant details. 

But from the user’s perspective, they are simply asking a question and receiving an answer.

Data visualization

The best augmented analytics solutions can not only offer robust data visualization capabilities, but also incorporate data visualizations into the automated production of reports to facilitate information sharing and collaborative decision-making.

While natural language is often a very useful way to articulate information, visualization is often the most intuitive way to draw comparisons and highlight patterns. Graphs, charts, diagrams, plots, heat maps and other types of data visualization can be a useful way to explore data and make connections that one might not think to explicitly incorporate into a query.

Historically, taking a natural language request as input and returning a sophisticated data visualization as output would require the sophisticated choreography of multiple models operating in an assembly line-like fashion. But the ongoing evolution of previously text-only LLMs into multimodal AI models that can seamlessly operate across different data modalities has further streamlined the versatility of augmented analytics platforms.

This allows for a dynamic approach to data analysis in which even non-technical users can openly explore connections and hypotheses, with results, recommendations and noteworthy insights made readily available in user-friendly interactive dashboards.

Automated insights and statistical analysis

Though NLP steals the spotlight, Gartner’s recent Magic Quadrant survey results indicate that the most sought-after capability for analytics and business intelligence (ABI) platforms is not natural language query, but automated insights. In other words, business users are more concerned with the results than with the process of obtaining those results.¹

The best augmented analytics solutions ease the burden of deciding exactly how to interrogate their data for insights, freeing business users to focus on how to act upon those insights. Beyond surface-level NLP capabilities, an LLM can act as a real-time decision-making engine. This empowers a modern augmented analytics platform to tailor analysis to the specific context of a user’s request in a much more dynamic way than would be possible with simple IF-THEN rules. 

For example, augmented analytics software can infer from the nature of a request what type of data will be examined and what type of analysis is desired, then intelligently suggest optimal data visualization schemes. Augmented analytics solutions can also run analyses across multiple forecasting models and highlight the predictions of the model offering the greatest certainty. Platforms can thereby offer insight into the prediction process, rather than simply spit out predictions.

Automated insights also empower proactive data analysis, surfacing outliers and emerging trends as they arise instead of waiting for the right query to bring them to light. For instance, automatic analytics tools could identify an unexpected dip in customer engagement metrics, alerting business users to some shortcoming of the customer experience so that it can be understood and addressed.

Predictive analytics and prescriptive analytics

An optimal analytics platform should be able to provide multiple lenses of data analysis, to understand the past and make informed decisions about the future. There are four key subsets of analytical insights, all of which are essential to the decision-making process.

• Descriptive analytics is concerned with objective analysis: What has happened or what is happening? For example, in the context of supply chains, descriptive analytics could explore where money is being spent or where there are inventory shortfalls.

• Diagnostic analytics aims to understand the past: Why things have happened. For example, analysis of previous customer behavior could be used to explain why an ongoing marketing initiative is falling short of expectations.

• Predictive analytics aims to predict the future: The probability that something will happen or the expected results for potential course of action. Predictive analytics typically represents the bedrock of a business intelligence operation, grounding decisions in a deeper understanding of their likely consequences.

• Prescriptive analytics aim to predict optimal actions: What should happen or how to maximize the likelihood of a desired outcome. The discipline of prescriptive modeling powers systems such as recommendation engines, combining predictive analytics with strong decision-making logic to identify the ideal way forward.